Adaptive evolution of non coding DNA and gene expression divergence in Drosophila

描述(由申请人提供)：越来越多的证据支持这样一种观点，即调控进化--基因在哪里和何时表达的进化--是高等生物体中模块化组织、功能多样化和新特征起源背后的主要遗传机制。在真核生物基因组中，大多数调控基因表达的元件都位于非编码DNA(即不编码蛋白质的DNA)中。最近的研究表明，果蝇基因组的大部分非编码部分在进化上受到限制，这意味着这些区域对生物体和S的健康非常重要，可能是实质性适应性进化的目标。我们建议使用新的计算和实验方法的组合来1)识别可能是反复适应性进化的目标的顺式调控非翻译转录区域(UTRs)，以及2)实验测试假定的功能替代对物种间基因表达差异水平的影响。我们将首先收集来自25个自然发生的拟盘孢子虫菌株的群体基因组变异性数据，包括全长转录本(~2.2Mb)和紧密相连的短内含子和编码序列的对照参考板(~5.8Mb)。我们将使用并进一步开发计算方法，利用这类种群遗传数据来识别积累了物种之间适应性序列差异的UTRs。具体地说，我们将探索使用等位基因频谱，并将这些新数据与黑腹夜蛾新兴群体基因组数据整合在一起，在多大程度上可以提高群体选择遗传测试的保真度。然后，我们将使用黑腹毛虫作为实验模型，从功能上验证基于计算方法的预测。具体地说，我们将使用转基因共定位方法来确定3&lt；UTR差异对基因表达差异的影响，并测试关于个体功能替代如何相互作用和促进基因表达变化的替代假设。这些实验反过来将被用来改进我们的计算预测方法。这项研究将确定新的顺式调控元件，开发新的方法来定位这些元件，并为基因调控变化如何导致新物种的进化和动物形式的多样性提供重要的见解。我们开发的计算方法和生物直觉将广泛适用于其他模型系统，包括人类。 公共卫生相关性：基因监管的变化有助于自然人口的适应，并影响人类疾病的易感性(Gilad等人)。2008年；戈比等人。2006)。尽管它们潜在的表型重要性，但作用于调控过程和基因表达水平的选择压力在很大程度上是未知的。我们的研究结合了计算和实验方法，研究自然选择如何作用于基因表达中有益和有害的功能差异背后的遗传变异。这项工作将大大提高我们对由基因错误表达引起的人类疾病的生物学理解。

DESCRIPTION (provided by applicant): A growing body of evidence supports the view that regulatory evolution - the evolution of where and when a gene is expressed - is the primary genetic mechanism behind the modular organization, functional diversification, and origin of novel traits in higher organisms. Most elements regulating gene expression in eukaryotic genomes reside in noncoding DNA (i.e. DNA that does not encode protein). Recent studies suggest that much of the noncoding portion of the Drosophila melanogaster genome is evolutionarily constrained, implying that these regions are important for an organism<s fitness and may be the target of substantial adaptive evolution. We propose to use a combination of novel computational and experimental approaches to 1) identify cis-regulatory untranslated transcribed regions (UTRs) that may have been targets of recurrent adaptive evolution and 2) experimentally test the effects of putatively functional substitutions on levels of gene expression divergence between species. We will begin by collecting population genomic variability data from 25 naturally occurring strains of D. simulans for all 5< and 3<UTRs with full length transcripts (~2.2Mb) and a control reference panel of closely-linked short introns and coding sequence (~5.8Mb). We will use and further develop computational methods to identify UTRs that have accumulated adaptive sequence divergence between species using population genetic data of this kind. Specifically, we will explore to what extent using the allelic frequency spectrum and integrating this new data with emerging population genomic data for D. melanogaster can improve the fidelity of population genetic tests for selection. We will then use D. melanogaster as an experimental model to functionally verify predictions based on computational methods. Specifically, we will use a transgene co-placement method to determine the effects of 3<UTR divergence on gene expression divergence and test alternative hypotheses about how individual functional substitutions interact and contribute to changes in gene expression. These experiments will, in turn, be used to refine our computational prediction methods. This research will identify new cis- regulatory elements, develop novel methodologies for mapping such elements and provide important insights into how gene regulatory changes have led to the evolution of new species and diversity in animal forms. The computational methods and biological intuitions we develop will be widely applicable to other model systems, including humans. PUBLIC HEALTH RELEVANCE: Changes in genetic regulation contribute to adaptations in natural populations and influence susceptibility to human diseases (Gilad et al. 2008; Gobbi et al. 2006). Despite their potential phenotypic importance, the selective pressures acting on regulatory processes and gene expression levels in particular are largely unknown. Our research combines computational and experimental approaches to study how natural selection acts on genetic variation underlying both beneficial and detrimental functional differences in gene expression. This work will significantly improve our understanding of biology of human diseases caused by the misexpression of genes.